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[model] switch to gptqmodel (#8108)

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hoshi-hiyouga
2025-05-19 22:25:40 +08:00
committed by GitHub
parent bc7f00f2c7
commit 45030ff803
9 changed files with 78 additions and 62 deletions
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48
src/llamafactory/model/model_utils/rope.py
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@@ -32,7 +32,7 @@ if TYPE_CHECKING:
logger = logging.get_logger(__name__)
def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments") -> None:
if model_args.rope_scaling is None:
return
@@ -40,30 +40,40 @@ def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_
logger.warning_rank0("Current model does not support RoPE scaling.")
return
rope_kwargs = {"rope_type": getattr(model_args.rope_scaling, "value", model_args.rope_scaling)} # handle enum
if model_args.model_max_length is not None:
if is_trainable and model_args.rope_scaling == RopeScaling.DYNAMIC:
if hasattr(config, "max_position_embeddings"):
old_max_length = getattr(config, "max_position_embeddings", None)
else:
logger.warning_rank0("Cannot find the max position embeddings in the config.")
return
if model_args.model_max_length is not None: # training
if model_args.model_max_length <= old_max_length:
logger.warning_rank0("Input length is smaller than max length. Disabling rope scaling.")
return
if model_args.rope_scaling == RopeScaling.DYNAMIC:
logger.warning_rank0(
"Dynamic NTK scaling may not work well with fine-tuning. "
"See: https://github.com/huggingface/transformers/pull/24653"
)
current_max_length = getattr(config, "max_position_embeddings", None)
if (not current_max_length) or model_args.model_max_length <= current_max_length:
logger.warning_rank0("Input length is smaller than max length. Disabling rope scaling.")
return
rope_factor = float(math.ceil(model_args.model_max_length / old_max_length))
else: # inference
rope_factor = 2.0
logger.info_rank0(f"Enlarge max model length from {current_max_length} to {model_args.model_max_length}.")
setattr(config, "max_position_embeddings", model_args.model_max_length)
rope_kwargs["factor"] = float(math.ceil(model_args.model_max_length / current_max_length))
if model_args.rope_scaling in [RopeScaling.DYNAMIC, RopeScaling.YARN]:
rope_kwargs["original_max_position_embeddings"] = current_max_length
elif model_args.rope_scaling == RopeScaling.LLAMA3:
rope_kwargs["original_max_position_embeddings"] = current_max_length
rope_kwargs["low_freq_factor"] = 1.0
rope_kwargs["high_freq_factor"] = 4.0
else:
rope_kwargs["factor"] = 2.0
rope_kwargs = {
"rope_type": getattr(model_args.rope_scaling, "value", model_args.rope_scaling), # handle enum
"factor": rope_factor,
}
setattr(config, "max_position_embeddings", old_max_length * rope_factor)
logger.info_rank0(f"Enlarge max model length from {old_max_length} to {old_max_length * rope_factor}.")
if model_args.rope_scaling in [RopeScaling.DYNAMIC, RopeScaling.YARN]:
rope_kwargs["original_max_position_embeddings"] = old_max_length
elif model_args.rope_scaling == RopeScaling.LLAMA3:
rope_kwargs["original_max_position_embeddings"] = old_max_length
rope_kwargs["low_freq_factor"] = 1.0
rope_kwargs["high_freq_factor"] = 4.0
setattr(config, "rope_scaling", rope_kwargs)
logger.info_rank0(